A Survey on Realms and Applications of Social Media Data Analysis
DOI:
https://doi.org/10.26438/ijcse/v6i11.844848Keywords:
Data Mining, Machine Learning, Natural Language Processing, Social MediaAbstract
The information era witnesses the creation of multimedia data, transfers and transactions in the order of millions. This data by virtue of their formats comes in varying sizes and differing temporal characteristics. The wealth of information carries potential both in terms of explicit content that is expressed and the implicit or hidden content. Processing the former is quite developed while the procedures and applications of working with the implicit knowledge are growing steadily. This paper aims to present a range of techniques from recent works pertaining to the processing and applicability of such data. The purpose of the survey is to bring to light the specific methods of social media data analysis in a concise and organized manner. Specifically, natural language processing, topic modelling, sentiment analysis and affective analysis have been identified as the overarching heads taken up by several recent researches. Finally, some observations pertaining to social media data analysis identified from several works are enlisted.
References
[1] A.Gandomi, and H.Murtaza "Beyond the Hype: Big Data Concepts, Methods, and Analytics." International Journal of Information Management Vol. 35, No. 2, pp.137-44, 2015.
[2] G.Coppersmith, C.Hilland, O.Frieder, and R.Leary. "Scalable Mental Health Analysis in the Clinical Whitespace via Natural Language Processing." 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, pp. 393-396, 2017.
[3] A.Schmidt and M.Wiegand, "A Survey on Hate Speech Detection Using Natural Language Processing." Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, Valencia, Spain, pp. 1-10, 2017.
[4] N.Mamgain, E.Mehta, A.Mittal, and G.Bhatt, "Sentiment Analysis of Top Colleges in India Using Twitter Data." 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), New Delhi, pp. 525-530, 2016.
[5] C. Nanda, M. Dua, “A Survey on Sentiment Analysis”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol 5, Issue 2, pp. 67-70, April, 2017.
[6] S.Poria, E.Cambria, A.Hussain, and G.B. Huang. "Towards an Intelligent Framework for Multimodal Affective Data Analysis." Neural Networks, Vol. 63, pp. 104-16, 2015.
[7] K.H.Lim, S.Karunasekera, A.Harwood, and L.Falzon. "Spatial-based Topic Modelling Using Wikidata Knowledge Base." 2017 IEEE International Conference on Big Data (Big Data),Boston, MA, USA, pp. 2009-2018, 2017.
[8] A.Sarker, R.Ginn, A.Nikfarjam, K.O.Connor, K.Smith, S.Jayaraman, T.Upadhaya, and G.Gonzalez. "Utilizing Social Media Data for Pharmacovigilance: A Review." Journal of Biomedical Informatics 54 , pp.202-12, 2015.
[9] D.T.Nguyen, K.A.A.Mannai, S.Joty, H. Sajjad, M.Imran,P.Mitra, “Robust classification of crisis-related data on social networks using convolutional neural networks”, Proceedings of the 11th International Conference on Web and Social Media, ICWSM, Montreal, Canada, pp. 632-635, 2017.
[10] A.Hausmann, T.Toivonen, R.Slotow, H.Tenkanen, A.Moilanen, V.Heikinheimo, and E.D.Minin. "Social Media Data Can Be Used to Understand Tourists’ Preferences for Nature-Based Experiences in Protected Areas." Conservation Letters Vol. 11, Issue. 1, pp.1-10, 2017.
[11] Z.A.Hamstead, D.Fisher, R.T.Ilieva, S.A.Wood, T.Mcphearson, and P.Kremer. "Geolocated Social Media as a Rapid Indicator of Park Visitation and Equitable Park Access." Computers, Environment and Urban Systems Vol. 72, pp.38-50,2018.
[12] A.S.Halibas, A.S.Shaffi, and M.A.K.V.Mohamed. "Application of Text Classification and Clustering of Twitter Data for Business Analytics." 2018 Majan International Conference (MIC), Muscat, Oman, pp. 1-7, 2018.
[13] R.S.Shirsath, V.A.Desale, A.D.Potgantwar, “Big Data Analytical Architecture for Real-Time Applications”, International Journal of Scientific Research in Network Security and Communication, Vol 5, Issue 4, August, 2017.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
